rivate equity (PE) investment had experienced a major boom in the nineties, (Gompers et al, 2016;Hung and Tsai, 2017). For instance, Hung and Tsai (2017) because of the boom of the venture capital in 1990, to the technology bust of 2000 to 2001, and the leveraged buyouts boom and bust in 2000. They also added that the resistance of lending standards in 2008 has caused the fall of the investments of the private equity industry. Nevertheless, it is well documented that private equity activity had a positive influence on economic development and entrepreneurship, (Bernoth and Colavecchio, 2014;Bernstein et al., 2016;Hellmann and Puri, 2000;Lerner, 2000). It positively affected the innovation by the introducing of new products, processes, or services on the market, the productivity and economic growth (i. e. to ensure the improvement of the production system, growth of high-tech start-up, development of skills that induce an effective use of existing knowledge), business dynamics and employment growth, (see, for instance, Belke et al., 2003;Engel and Keilbach, 2007;Gompers, 1994;Khan et al., 2018;Levine, 1997;Li et al., 2014;Milosevic, 2018;Ning et al., 2015;Puri and Zarutskie, 2012;Samila and Sorenson, 2011). Therefore, the determinants of private equity investment had received considerable attention (Bernoth and Colavecchio, 2014;Bernstein et al., 2016;Black and Gilson, 1998;Félix et al., 2013;Fenn et al., 1997;Gompers and Lerner, 1998;Precup, 2015) such as the real GDP growth, the market capitalization, the interest rate, the capital formation, the unemployment rate, the tax rate, the institutional and legal environment, the productivity index, the corruption index, the inflation rate, and the R&D expenditure.
The rest of the paper is structured as follows. Section two is divided into two parts. The first part presents the determinants and criteria that influence the private equity industry. The second part investigates the impact of the status quo. Section three describes our empirical method. Section four presents the dataset and the main statistics. Section five sets out our results.
Despite the existing literature, there is still no broad consensus on the influence of the status quo bias 2 on PE investments. It is important to mention that private equity investors are professionals who dedicate time to collect and analyze information before making an investment decision. For that reason, their decisions should be more rational (i.e. less affected by cognitive biases) than those of individuals. However, a review of studies undertaken looking at the existence of SQB at the institutional level (Elert et (Harbi and Toumia, 2020) had shown the vulnerable power of the status quo. We seek to develop a novel approach, to detect the SQB on the investment choice of PE investments across 24 countries for nine years (from 2007 to 2015) using the dynamic panel probit (respectively logit) model. In the present work, we extend the previous work of Harbi and Toumia (2020) in which they proved the existence of SQB in the venture capital industry at the country level. More precisely, we expect the presence of SQB in the private equity at the country level if the influence of the previous choice of the investment industry depends positively on the present one.
Section six concludes the paper and explains its main implications.
To date, the bulk of the academic and practitioner literature focuses on the determinants of private equity decisions and their role in economic growth. Bernoth and Colavecchio (2014) affirmed that private equity includes five investment stages which are venture capital, growth capital, replacement capital, rescue/turnaround, and buyouts. The role of this mode of financing is not limited to provide financial resources, but they also added value to their companies by assisting with a variety of services. They helped in establishing strategies, providing technical and commercial advice, attracting key personnel, enhancing the design process, and developing the portfolio companies, (Bygrave and Timmons, 1992;De Clercq, et al., 2006;Gompers and Lerner, 2001;Gorman and Sahlman, 1989;Lerner, 1995;Sapienza, 1992;Schwienbacher, 2008). Bloom et al. (2015) added that private equity-owned firms have significantly welled management practices 3 Given the fact that is not a broad consensus on the macroeconomic determinants of private equity investments in the Central and Eastern European countries, Bernoth and Colavecchio (2014) tried to fill this gap. They identified the determinants of private equity in Central and Eastern European and Western European countries from 2001 to 2011. They showed that economic activity, the inflation rate, equity market capitalization, unit labor costs, unemployment, the than other ownership groups (i. e. government, family, and privately-owned companies).
Understanding the factors which influence private equity has interested a lot of researchers. Among these factors, we state the real GDP growth, the market capitalization, the interest rate, the unemployment rate, the tax rate, and the R&D expenditure (Black and Gilson, 1998;Félix et al., 2013;Gompers and Lerner, 1998). Gompers and Lerner (1998) found that the GDP growth, interest, tax rate, and R&D expenditure are key factors in the evolution of venture capital; however, there is no relationship between the number of IPOs and the funds raised for the venture capital investments. In the same vein, Félix et al. (2013) To have an in-depth insight into the decisions of private equity firms, several researchers had analyzed the quasi-rational decision-making under risk and uncertainty by proposing several formal theories (e.g., prospect theory (Kahneman and Tversky, 1979) and regret theory (Bell, 1982)). Kahneman and Tversky (1979) had proposed the prospect theory, which is an alternative to the expected utility theory (also called Morgenstern-Von Neumann utility theory). It is a descriptive model of decision-making under risk by which the decisions made by individuals do not follow rational calculation. However, Bell's (1982) regret theory announced that an individual may recognize by observing the relevant outcomes that another alternative would have been preferable after deciding under uncertainty. This knowledge may yield a sense of loss or regret. Overall, the irrationality of the decision-maker has been analyzed in several types of research, and the explanations of this behavior have not gone unnoticed. From this perspective, many studies confirm the fact that both individuals and institutions do not behave rationally ( ) have focused on the impact of status quo in financial decisions. Among these studies, we find the work of Samuelson and Zeckhauser (1988). They demonstrated the presence of SQB when they examine the pension plans of Harvard employees. Indeed, it is well documented that mutual fund investors are subject to the status quo bias (Kempf and Ruenzi, 2006;Patel et al., 1991;Patel et al., 1994). In the same line, Agarwal et al., (2003) confirmed that hedge fund investors are influenced by the status quo bias. Barber et al., (2005) proved that investors have a great tendency to buy stocks they have already bought in the past. Agnew et al. (2003) found that U.S. investors prefer to maintain their initial asset allocation. Johnson et al. (1993) showed in their experimental study that the status quo intervenes in the choice of an insurance policy. Schweitzer (1995) addressed questionnaires to 400 staff at a large University. He found that status quo bias affects health care financing decisions. More concretely, individuals selected status quo alternatives more often than other alternatives. Madrian and Shea (2001) found a positive relationship between retirement savings and the status quo by using a database that contains information on 401(k) participation and savings behavior in the health care and insurance industry. Cronqvist and Thaler (2004) confirmed the consensus that inertia has also been found in U.S. 401(k) plans (Madrian and Shea, 2001;Samuelson and Zeckhauser, 1988). They found that recent returns influence the investments of participants. Indeed, the percentages of participants who remained with their portfolio during the three first years were 98.3, 97.3, and 96.9, respectively. Kempf and Ruenzi (2006) found strong evidence of the SQB by examining the U.S. equity mutual fund market. Moreover, Tekçe et al. (2016) argued that Turkish individual stock investors are subject to the status quo bias. Furthermore, they found that gender affects the choice of status quo alternatives. More precisely, female investors are more biased toward the status quo more than male investors. Burmeister and Schade (2007) extended previous studies (Busenitz and Barney, 1997; Parlich and Bagby, 1995) in which it is shown that entrepreneurs exhibit cognitive biases and they are more affected by cognitive biases than other individuals. They used an experimental study to compare the decisions of entrepreneurs with those made by students and bankers in an experimental study. They found that the three categories (i.e. entrepreneurs, students, and bankers) are affected by the status quo. However, bankers are more affected by the status quo than both entrepreneurs and students. Harbi and Toumia (2020) reported the influence of status quo bias on the venture capital industry at the country level. They used a dynamic panel probit (respectively logit) model for 24 OECD member countries from 2007 to 2015.
The main aim of our paper is to provide a deeper understanding of the influence of status quo bias in the private equity market. To do so, we look at the impact of the previous choice of the investment sector on the present ones. We follow the method used in the work of Harbi and Toumia (2020). More precisely, they expect the presence of SQB in the venture capital industry when there is a positive relationship between the actual choice of the investment industry and the present one. Similar to Harbi and Toumia (2020), we use the conditional maximum likelihood (CML) estimator proposed by Wooldridge (2005). This estimator allows the estimation of the dynamic panel probit model for the balanced panel. So, our model is the following: P(choice i,t = 1|choice i,t-1 , choice 2007 , VA i,t, , PEindex i,t t , R&D i,t,, c i ), t=2008,?,2015
(1)
==ð?"ð?" (? 1 VA it + ? 2 PEindex it + ? 3 R&D it + ?? 1 choice i,t-1 + c i ), t=2008,...,2015(2)Where the choice i,t is our binary dependent variable that equals 1 when the percentage of PE investment in ICT and "healthcare & LS" is the maximum and 0 otherwise., choice i,t-1 is the main independent variable of interest which is the choice of PE investment sector in the previous, ?? 1 is the coefficient of the lagged dependent variable, VA it denotes the added value by the activity of PE investment sector in a year "t", PE index it denotes PE Country Attractiveness Index in a year "t", R & D it represents the R&D expenditure of a country in a year "t", choice 2007 is the initial choice in 2007 and c i is the unobserved effect.
IV.
The data used comes from many online databases that contain annual information: Invest Europe/ PEREP_Analytics, World Bank, OECD (Organization for Economic Co-operation and Development), and IESE Business School. In sum, we get a data set that covers 24 4 European countries from 2007 to 2015. We are limited to this period because the reports provided by the Private Equity and Venture Capital association contain data only for the years 2007 to 2015. Table 2 presents the main variables and descriptive statistics. Table 3 The Pearson correlation coefficients are not significant and low for most pairs of variables. Among all the correlation coefficients, the only highest one is between the PE index i,t and the R & D i,t (correlation= -0.7330). Thus, there isn't a multicollinearity problem. This ascertainment was further supported by the use of VIF. Kennedy (1992) and Marquaridt (1970), revealed the existence of major multicollinearity when a VIF is greater than 10. In the same vein, O'Brien (2007) stated that high correlation may be problematic and should be treated with caution in the case where the VIFs are greater than 5 or 10 or 30. As shown in Table 3, the VIF is under 3, so there is a limited threat of multicollinearity. So, we may assume that the regression coefficients are well-conditioned estimated and the multiple correlations with other variables aren't high.
V. Also, the initial choice in 2007 (choice 2007 ) is not significant. Furthermore, we find that the added value by activity, the private equity country attractiveness index, and the research & development expenditures have a positive significant impact on the choice of the investment sector.
This finding is in line with previous studies (Gompers and Lerner, 1998;Groh et al., 2010;Harbi and Toumia, 2020;Hellmann and Puri, 2000;Lahr and Mina, 2016). The p-value of the Wald test and LR test are less than 5%, implying that the coefficients are not simultaneously equal to zero. Moreover, the rho differs from zero, concluding the difference between the panel estimator and the pooled estimator.
However, this finding suffers from a possible limitation notably related to the consideration of the regression results of a combined variable (ICT + healthcare & LS). These two sectors may have different economic drivers that may influence our results. For the sake of clarity and better analysis, we examined each PE investment sector as a separate dependent variable (see table 5). Column 2 in Table 5 presents the results of regression when the binary dependent variable is equal to 1 if the percentage of PE investment in ICT is the maximum and 0 otherwise. Column 4 in Table 5 presents the case when the dependent variable is 1 if the percentage of PE investment in healthcare & LS is the maximum and 0 otherwise. Similar to previous results, we find that the previous choice of divestment is not significant for both models (see table 5).
So far, there is a huge literature that enumerates the determinants of private equity activity because of its major impact on economic development and entrepreneurship, (Bonini and Alkan, 2012;Gompers and Lerner, 1998). Nevertheless, the influence of the status quo on the private equity investments' decision has never been discussed beforehand. Hence our contribution is unique in trying to filling the gap in the existing literature by investigating if private equity firms are subject to the status quo. Contrary to the work of Harbi and Toumia (2020), we find that the previous choice of the investment industry doesn't depend on the present one.
Although this study provided an empirical model to show how private equity firms make their decisions, it is essential to recognize the influence of other factors that do not change over time that may influence the decision of private equity firms. Thus we tried to control the influence of some variables which are recognized as determinants of the private equity market. Indeed, following previous contributions (Gompers and Lerner, 1998;Groh et al., 2010;Harbi and Toumia, 2020;Hellmann and Puri, 2000;Lahr and Mina, 2016), the added value by activity, the private equity country For organizations, our findings can help to further understanding why the status quo occurs. Using the results of our research, private equity investors may better frame their decisions to overcome this bias. Moreover, the status quo is considered a critical barrier to organizational change and development. Investors may not recognize that they are too attached to their ideas, opinions, and decision-making. Thus they may interpret the status quo as signaling success and they feel no need to search for novel perspectives or ideas. Being aware of this limit, it would be advisable for managers to adopt a proactive behavior ( Since we are entirely interested in our analysis on the impact of the status quo, we have not included other macroeconomic determinants of the private equity investment industry. We only include variables that measure economic activity. Hence, our contribution provides a better understanding of the behavior of private equity firms, however, our results may not be considered as definitive. Indeed our sample is composed of European countries; although we are not sure that our findings would also hold with, e.g., African countries, Asian countries, or American countries. So, we should not underestimate the relevance of country differences, that's why; we recommend performing other studies to confirm our result.
| institutional and legal environment influence the private |
| equity activity. Precup (2015) extended previous |
| literature by identifying the major determinants of the |
| European private equity market. He used data on 27 |
| European countries in his empirical panel analysis from |
| 2000 to 2013. Among several determinants already |
| tested in previous works (GDP growth, Market |
| Capitalization, |
| investigated the determinants of |
| the European private equity market for a group of 23 |
| European countries for the period 1998-2003. They |
| found that the GDP growth, market capitalization, the |
| number of IPOs, the number of mergers and |
| acquisitions (M&A), the interest, and the unemployment |
| rate were considered as drivers of the private equity |
| market in Europe. Contrary to Gompers and Lerner |
| (1998), they found that R&D expenditure has no impact |
| on private equity activity. |
| shows the percentage of PE | ||||
| investment | for | Information | & | Communication |
| Technology (ICT) and healthcare & life sciences (LS). A | ||||
| closer look at these percentages reveals a preference | ||||
| for both sectors. So, we construct a variable "choice of | ||||
| PE investment sector". It takes a value of 1 if the | ||||
| percentage of PE investment in ICT and "healthcare and | ||||
| LS" is the maximum and 0 otherwise. | ||||
| 2013 | 33.2% | 37.8% | 23.7% | 84% | 59.5% | 59.6% |
| 2014 | 29.5% | 49.7% | 34.3% | 100% | 5% | 64.6% |
| 2015 | 56.5% | 21.1% | 67.7% | 100% | 1.7% | 39.1% |
| Italy | Hungary | Portugal | Poland | Czech Republic | Luxembourg | |
| 2007 | 9.8% | 15.3% | 28.1% | 21.9% | 49.4% | 11.8% |
| 2008 | 28% | 94.8% | 6.1% | 12.7% | 75.1% | 59.9% |
| 2009 | 17.4% | 95.1% | 37.7% | 55.1% | 15.4% | 80.2% |
| 2010 | 39.9% | 32.4% | 5.7% | 16.9% | 45.1% | 7.6% |
| 2011 | 16.1% | 17.4% | 14.4% | 45.8% | 16.9% | 16.9% |
| 2012 | 31.7% | 29% | 10.7% | 16.3% | 85.7% | 30.7% |
| 2013 | 20.1% | 16% | 8.7% | 23.7% | 16.9% | 12.9% |
| 2014 | 5.7% | 38.6% | 22.8% | 50.1% | 76.1% | 45.3% |
| 2015 | 10.9% | 6.6% | 38.1% | 12.1% | 20.3% | 0.6% |
| Ireland | Netherlands | France | Finland | Norway | United Kingdom | |
| 2007 | 72.8% | 30.8% | 24.3% | 43.9% | 40.7% | 29.7% |
| 2008 | 36.1% | 13.4% | 37.2% | 38.2% | 27% | 28.5% |
| 2009 | 19.8% | 34.8% | 32.1% | 44.5% | 22.6% | 30.1% |
| 2010 | 31.7% | 21.9% | 35.5% | 53.5% | 65.4% | 25.4% |
| 2011 | 35.7% | 12.5% | 27.5% | 54.6% | 24.9% | 42.7% |
| 2012 | 35.7% | 27.1% | 17.4% | 19.2% | 40.7% | 32.8% |
| 2013 | 67.9% | 34.8% | 18.9% | 75.8% | 17% | 28.2% |
| 2014 | 93% | 72.4% | 39.9% | 34.7% | 34% | 31.3% |
| 2015 | 28.5% | 23.6% | 31.2% | 16.1% | 44.6% | 25.8% |
| Nonetheless, we also include other variables | ||||||
| such as the added value by activity, the private equity | ||||||
| country attractiveness index, and the research & | ||||||
| development expenditures (see for a review, Gompers | ||||||
| and Lerner, 1998; Groh et al., 2010; Harbi and Toumia, | ||||||
| 2020; Hellmann and Puri, 2000; Lahr and Mina, 2016). | ||||||
| 2012 Variable | 31.3% Source | 17.4% N | 14.3% Mean | 0% Descriptive statistics 79.1% SD Min | 64% Max | |
| choice i,t | 216 | 0.514 | 0.501 | 0 | 1 | |
| choice i,t-1 | 192 | 0.526 | 0.501 | 0 | 1 | |
| choice 2007 | 192 | 0.536 | 0.500 | 0 | 1 | |
| VA i,t | 216 | 3.513 | 8.611 | -19.47 | 44.47 | |
| PEindex i,t | 216 | 30.041 | 18.934 | 2 | 90 | |
| R&D i,t | 216 | 1.720 | 0.899 | 0.382 | 3.750 | |
| Year 2021 | |||
| 43 | |||
| Volume XXI Issue VI Version I | |||
| ( ) A | |||
| choice i,t-1 choice 2007 VA i,t PEindex i,t R&D i,t Constant Number of observation Number of groups Log pseudo-likelihood Wald chi2(5) Prob>chi2 LR chi2(5) Prob>chi2 Sigma u Rho | Wooldridge's (2005) Probit VCE robut Wooldridge's (2005) Logit VCE robut Estimates (P >| z|) dy/dx Estimates (P >| z|) dy/dx -0.275 (0.182) -0.100 -0.431 (0.191) -0.096 0 .344 (0.091) 0.125 0.571 (0.090) 0.127 0.046** (0.009) 0.017 0.081* (0.014) 0.018 0.019** (0.010) 0.302* (0.013) 0.0069 0.110 0.031* (0.011) 0.482* (0.016) 0.007 0.107 -1.229** (0.006) -2.024** (0.006) 192 192 24 24 -122.4025 -122.1760 20.04 (0.0012) 18.30 0.0026 21.20 0.0007 21.65 0.0006 0.0017674 0.0047433 3.12e -06 6.84e -06 | Global Journal of Management and Business Research | |
| AIC | 258.805 | 258.351 | |
| BIC | 281.607 | 281.154 | |
| Legend: * p<0.05; ** p<0.01; *** p<0.001 | |||
| © 2021 Global Journals | |||
| ICT VCE Robust | LS VCE Robust | |||
| Estimates (P >| z|) | dy/dx | Estimates (P >| z|) | dy/dx | |
| choice i,t-1 | -0.474 (0.098) | -0.141 | -0.139 (0.730) | -0.029 |
| choice 2007 | 0.219 (0.371) | 0.0656 | 0.605 (0.096) | 0.126 |
| VA i,t | 0.031 (0.084) | 0.0093 | 0.044** (0.010) | 0.009 |
| PEindex i,t R&D i,t | 0.0120 (0.255) 0.111 (0.563) | 0.0036 0.0333 | 0.0268** (0.002) 0.312 (0.161) | 0.0055 0.0651 |
| Constant | -1.2967* (0.046) | -2.599*** (0.000) | ||
| Number of observation | ||||
| Number of groups | 24 | 24 | ||
| Log pseudo-likelihood | -103.81708 | -72.913689 | ||
| Wald chi2(5) | 7.11 | 12.41 | ||
| Prob>chi2 | 0.2126 | 0.0296 | ||
| LR chi2(5) | 8.26 | 13.64 | ||
| Prob>chi2 | 0.1425 | 0.0180 | ||
| Sigma u | 0.293 | 0.0005 | ||
| Rho | 0.079 | 2.55 e -07 | ||
| AIC | 221.634 | 159.827 | ||
| BIC | 244.436 | 182.629 | ||
| Le gend: * p<0.05; ** p<0.01; *** p<0.001 | ||||
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